AI prediction of response to pre-surgery chemoimmunotherapy for esophageal cancer
Multimodal Deep Learning for Predicting Treatment Response to Neoadjuvant Chemoimmunotherapy in Esophageal Cancer
Central South University · NCT07063901
This project tests whether combining clinical records, CT scans, pathology, and lab results with deep learning can predict which patients with locally advanced esophageal cancer will respond to neoadjuvant chemoimmunotherapy before surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 200 (estimated) |
| Sex | All |
| Sponsor | Central South University (other) |
| Locations | 1 site (Changsha, Hunan) |
| Trial ID | NCT07063901 on ClinicalTrials.gov |
What this trial studies
This observational cohort at The Second Xiangya Hospital of Central South University uses multimodal clinical data — including demographics, medical history, imaging, pathology, and laboratory tests — to train deep learning models to predict pathological complete response (pCR) after neoadjuvant chemoimmunotherapy in esophageal cancer. The models will also aim to identify early patients with suboptimal responses (stable disease or progressive disease) prior to surgery. The study uses retrospective clinical and imaging records from patients who received neoadjuvant chemoimmunotherapy and have complete pre- and post-treatment imaging. Models will be developed and internally validated on the institutional dataset to generate predictive tools for potential clinical decision support.
Who should consider this trial
Good fit: Ideal candidates are patients with histologically confirmed esophageal cancer who received neoadjuvant chemoimmunotherapy and have complete, high-quality pre- and post-treatment imaging and clinical records.
Not a fit: Patients with missing or poor-quality CT images, incomplete clinical data, concurrent other malignancies, or who refused recommended surgery are unlikely to benefit from the predictive models or be eligible for inclusion.
Why it matters
Potential benefit: If successful, these models could help clinicians identify patients likely to achieve pCR or those unlikely to benefit from neoadjuvant chemoimmunotherapy, enabling more personalized treatment planning and earlier changes in care.
How similar studies have performed: Multimodal AI approaches have shown promising predictive results in other tumor types, but applying deep learning to predict pCR after neoadjuvant chemoimmunotherapy in esophageal cancer remains relatively novel and less validated.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Patients with histologically confirmed esophageal cancer based on biopsy results; 2. Patients recommended for neoadjuvant chemoimmunotherapy following multidisciplinary team (MDT) discussion or evaluation by thoracic surgery specialists; 3. Patients who received neoadjuvant chemoimmunotherapy; 4. Patients with complete imaging data before and after neoadjuvant treatment. Exclusion Criteria: 1. Patients deemed eligible for surgery by the thoracic surgery team but who refused surgical treatment; 2. Patients with missing or poor-quality CT images; 3. Patients with concurrent malignancies other than esophageal cancer; 4. Patients with incomplete clinical data.
Where this trial is running
Changsha, Hunan
- The Second Xiangya Hospital of Central South University — Changsha, Hunan, China (RECRUITING)
Study contacts
- Study coordinator: Chen Chen
- Email: chenchen1981412@csu.edu.cn
- Phone: +8673185295188
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Esophagus Cancer